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student to work within the ADaM project (Autonomous workflows for Data-driven first-principles Modelling). The project will leverage Large Language Models (LLMs) as active software agents to help automate
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and molecular modelling, quantum dynamics and quantum information, complex systems, biophysics, and multidisciplinary challenges in, e.g., computational cardiology. 3.Photonics https://www.tuni.fi
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: Computational materials modeling: DFT, molecular dynamics, phase-field modeling, or multiscale simulations. Data-driven materials discovery: ML models for property prediction, materials design, or synthesis
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researcher for a two-year position within the ADaM project (Autonomous workflows for Data-driven first-principles Modelling). By leveraging agentic Large Language Models (LLMs), the project will develop
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of the project 'SOS_PRODEHESAMONTADO: Comprehensive sustainability and resilience models of the dehesa-montado against climate threats' (0086_SOS_PRODEHESAMONTADO_4_E), co-financed by the European Regional
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help reconstruct biodiversity time series and assess how forest management impacts species dynamics. The position will involve lab and field work, bioinformatics, and ecological modeling. Results will
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such as combinatorial sampling, molecular dynamics, and quantum computing. Certifications/Licenses Required Knowledge, Skills, and Abilities Ability to generate and analyze protein or molecular structural
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design of quantum platforms and models to improve upon existing error correcting codes, while leveraging platform-specific physics knowledge, be it in ultracold neutral atoms, trapped ions
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, molecular dynamics) to investigate physical properties and phenomena in nanomaterials. Analyze and interpret computational results; prepare high-quality manuscripts and research reports. Present research
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. The specific focus of this position will be on the theoretical design of quantum platforms and models to improve upon existing error correcting codes, while leveraging platform-specific physics knowledge, be it